This project uses skills from the Udacity Cloud Devops Nanodegree course to operationalize a Machine Learning Microservice API.
Model: a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. The data was initially taken from Kaggle, on the data source site. This project operationalizes a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.
Project goal: operationalize this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications.
Activities:
- Test the project code using linting
- Complete a Dockerfile to containerize this application
- Deploy the containerized application using Docker and make a prediction
- Improve the log statements in the source code for this application
- Configure Kubernetes and create a Kubernetes cluster
- Deploy a container using Kubernetes and make a prediction
- Upload a complete Github repo with CircleCI to indicate that the code has been tested
Detailed project rubric, here.
- Create a virtualenv and activate it
- Run
make installto install the necessary dependencies
- Standalone:
python app.py - Run in Docker:
./run_docker.sh - Run in Kubernetes:
./run_kubernetes.sh
- Setup and Configure Docker locally
- Setup and Configure Kubernetes locally
- Create Flask app in Container
- Run via kubectl